ArticleScience advances2023
Accurate global machine learning force fields for molecules with hundreds of atoms.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Automating Model Building for SPM Images of Biomolecules Using MISO.Journal of chemical information and modeling · 2026Article
- Modeling dual-range atomic interactions with physicochemical principles for molecular force fields.Bioinformatics (Oxford, England) · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- A Controlled Study of Physics-Informed Auxiliary Supervision and Scalar Triplet Attention in Equivariant Molecular Force Fields.Molecules (Basel, Switzerland) · 2026Article
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention.Nature communications · 2026Article
- Extending the range of graph neural networks with global encodings.Nature communications · 2026Article
- AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.Angewandte Chemie (International ed. in English) · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Quantum mechanics in drug design: Progress, challenges, and future frontiers.Communicative & integrative biology · 2026Review
- Defects and defect-mediated engineering of two-dimensional materials: challenges and open questions.Beilstein journal of nanotechnology · 2026Article
- Supersaturation, Nucleation, and Phase Separation of Mesoscopic Systems.Journal of the American Chemical Society · 2025Article
- PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.Nature communications · 2025Article
- Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.Scientific data · 2025Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global machine learning force fields, with the capacity to capture collective interactions in molecular systems, now scale up to a few dozen atoms due to considerable growth of model complexity with system size. For larger molecules, locality assumptions are introduced, with the consequence that nonlocal interactions are not described. Here, we develop an exact iterative approach to train global symmetric gradient domain machine learning (sGDML) force fields (FFs) for several hundred atoms, without resorting to any potentially uncontrolled approximations. All atomic degrees of freedom remain correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of sGDML on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond path-integral molecular dynamics simulations for supramolecular complexes in the MD22 dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.